babbel product analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/debaggi/DTCW-BZ-DA-ML-DS/blob/main/babbel%20product%20analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gu3hlMrcp6Xx"
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      "source": [
        "# Задача\n",
        "\n",
        "> Ваша команда разработчиков хочет изучить новые способы активизации учащихся в течение нескольких недель обучения с Babbel. Перед вами стоит задача проанализировать, как учащиеся Babbel осваивают наш учебный\n",
        "процесс в первые два месяца после получения полного доступа, и определить возможные пути\n",
        "улучшения.\n",
        "Пожалуйста, используйте прилагаемый набор данных и следующие руководящие вопросы для анализа ранних\n",
        "пройдите путь вовлечения в процесс обучения и укажите на потенциал оптимизации:\n",
        "\n",
        "- 1. Учитывая имеющиеся у вас данные, как бы вы описали поведение пользователя, которое\n",
        "указывает на успешную активацию?\n",
        "- 2. Чем отличается процесс раннего вовлечения в разные сегменты обучающихся?\n",
        "- 3. Как используются интерактивные занятия в сочетании с другими видами обучения? Ведут\n",
        "ли подписчики в режиме реального времени что-то иное, чем другие подписчики?\n",
        "- 4. Предположим, что команда планирует разработать экспериментальную программу обучения, направленную на\n",
        "активизацию новых учащихся в течение первых трех недель после регистрации. Команда хочет\n",
        "Проводить A / B тестирование и повторять использование функции каждые 2 недели, чтобы они не могли оптимизировать ее на основе\n",
        "трехнедельного запаздывания. Основываясь на вашем опыте и предоставленном наборе данных, что\n",
        "что бы вы посоветовали команде сделать? Есть ли какие-либо риски, о которых им следует знать?"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHLWdwMYZOrO"
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      "source": [
        "https://drive.google.com/drive/folders/1yrcaRwEsFn1rVaMhH3kiH0FmsIXqhEt-?usp=drive_link"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-9nAjBqiqGrX"
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      "source": []
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        "id": "8zfCTsdkqGkT"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okLFwIQTqdBG"
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      "source": [
        "Задачи:\n",
        "* составить портреты клиентов\n",
        "* выявить паттерны обучения клиентов с успешной активацией\n",
        "* как ведут себя клиенты в первые два месяца после активации обучения"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AiJvOW_Mq5hQ"
      },
      "source": [
        "### Комментарии\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "из анализируемого датасета удаляю ошибочные данные\n",
        "```\n",
        "\n",
        "```\n",
        "отфильровываю записи, удовлетворяющие условию:\n",
        "age == missing AND motivation == missing\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "d8izsdaesCX_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gk0pAinrzbfA"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIwHVOb26pGa"
      },
      "source": [
        "###  Обработка датасета learn_sessions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "TapR2Cg36hmf"
      },
      "outputs": [],
      "source": [
        "path_dataset = '/content/learning_sessions.csv'\n",
        "learn_sessions = pd.read_csv(path_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "lYUVum4v6la_"
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      "outputs": [],
      "source": [
        "# выбираю качественные и количественные атрибуты из датасета learning_session\n",
        "learn_sessions = learn_sessions[['uuid', 'subscription_started_at', 'age', 'motivation',\n",
        "       'subscription_type', 'geo_area', 'learning_activity', 'learning_os',\n",
        "       'learning_language', 'session_ended_as', 'session_started_at',\n",
        "       'session_local_started_at', 'time_spent']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "SxzpasB_ydzi"
      },
      "outputs": [],
      "source": [
        "# привожу атрибуты даты-времени к типу datetime\n",
        "learn_sessions['subscription_started_at'] = pd.to_datetime(learn_sessions['subscription_started_at'])\n",
        "learn_sessions['session_started_at'] = pd.to_datetime(learn_sessions['session_started_at'])\n",
        "learn_sessions['session_local_started_at'] = pd.to_datetime(learn_sessions['session_local_started_at'])"
      ]
    },
    {
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      "execution_count": 8,
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              "                               uuid subscription_started_at       age  \\\n",
              "1  072d948379bbfab373951e1ba4f039e3     2022-01-18 04:29:53  17_minus   \n",
              "2  bac98c42570186f0b57ff66f38d99314     2022-03-01 06:04:41     45-54   \n",
              "6  2ddb86acc0bbfd6b1dd2e9b63963f8b1     2022-02-27 13:32:39     35-44   \n",
              "\n",
              "       motivation subscription_type geo_area learning_activity learning_os  \\\n",
              "1  Friends_Family              core      USA            lesson         iOS   \n",
              "2            Work              core      USA            review         iOS   \n",
              "6          Travel              core      USA            review         iOS   \n",
              "\n",
              "  learning_language session_ended_as  session_started_at  \\\n",
              "1               DEU        completed 2022-01-18 04:54:18   \n",
              "2               QMS        completed 2022-03-05 23:57:31   \n",
              "6               ITA          missing 2022-04-07 09:36:08   \n",
              "\n",
              "  session_local_started_at  time_spent  \n",
              "1      2022-01-17 20:54:18    2.431191  \n",
              "2      2022-03-05 15:57:31    2.265713  \n",
              "6      2022-04-07 05:36:08    4.052059  "
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              "      <td>2022-01-18 04:29:53</td>\n",
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              "variable_name": "filtered_df"
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          "metadata": {},
          "execution_count": 8
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      ],
      "source": [
        "# отфильтровываю из датасета записи, в которых age = missing & motivation = missing\n",
        "filtered_df = learn_sessions[(learn_sessions['age']!='missing')&(learn_sessions['motivation']!='missing')]\n",
        "filtered_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# выгружаю датасет filtered_df в csv-файл\n",
        "filtered_df.to_csv('filtered.csv')"
      ],
      "metadata": {
        "id": "MygrW0hRsfgn"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqVezc_xRd36",
        "outputId": "1da52968-71f7-45f3-b022-bebd63ed6694"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2900"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "len(filtered_df['uuid'].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "IPetM-Rh5MKt"
      },
      "outputs": [],
      "source": [
        "# выборка уникальных uuid\n",
        "unique_uuid = filtered_df['uuid'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# записываю уникальные uuid в csv-файл\n",
        "unique_uuid_df = pd.DataFrame(unique_uuid)\n",
        "len(unique_uuid_df)\n",
        "# unique_uuid.to_csv('unique_uuid.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vH66GEhvq8_y",
        "outputId": "05edbb58-322c-4782-d6f0-0a562020c5a2"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2900"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "PAy6NwjX1TlW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "b987bad9-5550-4d33-9d88-a44fe215bee3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    uuid subscription_started_at       age  \\\n",
              "1       072d948379bbfab373951e1ba4f039e3     2022-01-18 04:29:53  17_minus   \n",
              "2       bac98c42570186f0b57ff66f38d99314     2022-03-01 06:04:41     45-54   \n",
              "6       2ddb86acc0bbfd6b1dd2e9b63963f8b1     2022-02-27 13:32:39     35-44   \n",
              "8       67e301facca5189116b29f8df9a175f7     2022-02-20 02:14:38  17_minus   \n",
              "11      ff00c8aea5bc4e6ba9431aded0d74c89     2022-01-17 15:35:16     35-44   \n",
              "...                                  ...                     ...       ...   \n",
              "272678  0a0cccb1dda06314fbee663f4473bad6     2022-04-29 14:16:01     18-24   \n",
              "275076  c0c6c736803705a1be67720d96c4ca89     2022-05-11 15:40:47     35-44   \n",
              "281137  af68a9c673884615f9880ff7b9943989     2022-05-07 11:28:36     35-44   \n",
              "281771  8eb72efa1bf3659c249e200810e30a19     2022-05-08 10:35:48     18-24   \n",
              "282695  af57b50ffbc943b15d70dd841b039d90     2022-02-09 13:51:00     45-54   \n",
              "\n",
              "             motivation subscription_type geo_area learning_activity  \\\n",
              "1        Friends_Family              core      USA            lesson   \n",
              "2                  Work              core      USA            review   \n",
              "6                Travel              core      USA            review   \n",
              "8              Interest              core      USA             audio   \n",
              "11               Travel              core      USA            lesson   \n",
              "...                 ...               ...      ...               ...   \n",
              "272678           Skills              core      GSM            lesson   \n",
              "275076             Work              core      GSM            lesson   \n",
              "281137  Boost_my_career              core      GSM            lesson   \n",
              "281771             Work              core      GSM            lesson   \n",
              "282695         Interest              core      ESM            review   \n",
              "\n",
              "       learning_os learning_language session_ended_as  session_started_at  \\\n",
              "1              iOS               DEU        completed 2022-01-18 04:54:18   \n",
              "2              iOS               QMS        completed 2022-03-05 23:57:31   \n",
              "6              iOS               ITA          missing 2022-04-07 09:36:08   \n",
              "8              iOS               FRA        completed 2022-02-20 17:39:16   \n",
              "11             iOS               QMS        completed 2022-01-24 16:01:33   \n",
              "...            ...               ...              ...                 ...   \n",
              "272678     Android               ENG          matched 2022-04-29 18:09:15   \n",
              "275076         iOS               FRA        completed 2022-05-20 06:05:24   \n",
              "281137         iOS               ENG        completed 2022-05-07 11:32:16   \n",
              "281771         iOS               FRA        completed 2022-05-20 10:55:47   \n",
              "282695     Android               SPA        completed 2022-02-27 16:32:09   \n",
              "\n",
              "       session_local_started_at  time_spent  \n",
              "1           2022-01-17 20:54:18    2.431191  \n",
              "2           2022-03-05 15:57:31    2.265713  \n",
              "6           2022-04-07 05:36:08    4.052059  \n",
              "8           2022-02-20 09:39:16    3.495342  \n",
              "11          2022-01-24 11:01:33    3.260316  \n",
              "...                         ...         ...  \n",
              "272678      2022-04-29 20:09:15    1.619296  \n",
              "275076      2022-05-20 08:05:24    9.583700  \n",
              "281137      2022-05-07 13:32:16    0.409841  \n",
              "281771      2022-05-20 12:55:47    9.675295  \n",
              "282695      2022-02-27 16:32:09    9.825057  \n",
              "\n",
              "[2900 rows x 13 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>uuid</th>\n",
              "      <th>subscription_started_at</th>\n",
              "      <th>age</th>\n",
              "      <th>motivation</th>\n",
              "      <th>subscription_type</th>\n",
              "      <th>geo_area</th>\n",
              "      <th>learning_activity</th>\n",
              "      <th>learning_os</th>\n",
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              "      <th>session_ended_as</th>\n",
              "      <th>session_started_at</th>\n",
              "      <th>session_local_started_at</th>\n",
              "      <th>time_spent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>072d948379bbfab373951e1ba4f039e3</td>\n",
              "      <td>2022-01-18 04:29:53</td>\n",
              "      <td>17_minus</td>\n",
              "      <td>Friends_Family</td>\n",
              "      <td>core</td>\n",
              "      <td>USA</td>\n",
              "      <td>lesson</td>\n",
              "      <td>iOS</td>\n",
              "      <td>DEU</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-01-18 04:54:18</td>\n",
              "      <td>2022-01-17 20:54:18</td>\n",
              "      <td>2.431191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>bac98c42570186f0b57ff66f38d99314</td>\n",
              "      <td>2022-03-01 06:04:41</td>\n",
              "      <td>45-54</td>\n",
              "      <td>Work</td>\n",
              "      <td>core</td>\n",
              "      <td>USA</td>\n",
              "      <td>review</td>\n",
              "      <td>iOS</td>\n",
              "      <td>QMS</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-03-05 23:57:31</td>\n",
              "      <td>2022-03-05 15:57:31</td>\n",
              "      <td>2.265713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2ddb86acc0bbfd6b1dd2e9b63963f8b1</td>\n",
              "      <td>2022-02-27 13:32:39</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Travel</td>\n",
              "      <td>core</td>\n",
              "      <td>USA</td>\n",
              "      <td>review</td>\n",
              "      <td>iOS</td>\n",
              "      <td>ITA</td>\n",
              "      <td>missing</td>\n",
              "      <td>2022-04-07 09:36:08</td>\n",
              "      <td>2022-04-07 05:36:08</td>\n",
              "      <td>4.052059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>67e301facca5189116b29f8df9a175f7</td>\n",
              "      <td>2022-02-20 02:14:38</td>\n",
              "      <td>17_minus</td>\n",
              "      <td>Interest</td>\n",
              "      <td>core</td>\n",
              "      <td>USA</td>\n",
              "      <td>audio</td>\n",
              "      <td>iOS</td>\n",
              "      <td>FRA</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-02-20 17:39:16</td>\n",
              "      <td>2022-02-20 09:39:16</td>\n",
              "      <td>3.495342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ff00c8aea5bc4e6ba9431aded0d74c89</td>\n",
              "      <td>2022-01-17 15:35:16</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Travel</td>\n",
              "      <td>core</td>\n",
              "      <td>USA</td>\n",
              "      <td>lesson</td>\n",
              "      <td>iOS</td>\n",
              "      <td>QMS</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-01-24 16:01:33</td>\n",
              "      <td>2022-01-24 11:01:33</td>\n",
              "      <td>3.260316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272678</th>\n",
              "      <td>0a0cccb1dda06314fbee663f4473bad6</td>\n",
              "      <td>2022-04-29 14:16:01</td>\n",
              "      <td>18-24</td>\n",
              "      <td>Skills</td>\n",
              "      <td>core</td>\n",
              "      <td>GSM</td>\n",
              "      <td>lesson</td>\n",
              "      <td>Android</td>\n",
              "      <td>ENG</td>\n",
              "      <td>matched</td>\n",
              "      <td>2022-04-29 18:09:15</td>\n",
              "      <td>2022-04-29 20:09:15</td>\n",
              "      <td>1.619296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275076</th>\n",
              "      <td>c0c6c736803705a1be67720d96c4ca89</td>\n",
              "      <td>2022-05-11 15:40:47</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Work</td>\n",
              "      <td>core</td>\n",
              "      <td>GSM</td>\n",
              "      <td>lesson</td>\n",
              "      <td>iOS</td>\n",
              "      <td>FRA</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-05-20 06:05:24</td>\n",
              "      <td>2022-05-20 08:05:24</td>\n",
              "      <td>9.583700</td>\n",
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              "    <tr>\n",
              "      <th>281137</th>\n",
              "      <td>af68a9c673884615f9880ff7b9943989</td>\n",
              "      <td>2022-05-07 11:28:36</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Boost_my_career</td>\n",
              "      <td>core</td>\n",
              "      <td>GSM</td>\n",
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              "      <td>0.409841</td>\n",
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              "      <th>281771</th>\n",
              "      <td>8eb72efa1bf3659c249e200810e30a19</td>\n",
              "      <td>2022-05-08 10:35:48</td>\n",
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              "      <td>2022-05-20 12:55:47</td>\n",
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              "      <td>af57b50ffbc943b15d70dd841b039d90</td>\n",
              "      <td>2022-02-09 13:51:00</td>\n",
              "      <td>45-54</td>\n",
              "      <td>Interest</td>\n",
              "      <td>core</td>\n",
              "      <td>ESM</td>\n",
              "      <td>review</td>\n",
              "      <td>Android</td>\n",
              "      <td>SPA</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-02-27 16:32:09</td>\n",
              "      <td>2022-02-27 16:32:09</td>\n",
              "      <td>9.825057</td>\n",
              "    </tr>\n",
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              "<p>2900 rows × 13 columns</p>\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-31f90299-ca9f-408d-b961-478adc0990df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "only_unique_uuid",
              "summary": "{\n  \"name\": \"only_unique_uuid\",\n  \"rows\": 2900,\n  \"fields\": [\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2900,\n        \"samples\": [\n          \"88a52478da5ffcdf7b3312236ded9ae2\",\n          \"7ef8ba715889ebd793954f7c06e73ab7\",\n          \"feff67a1164f6c738ee65df205bc123c\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subscription_started_at\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-01-01 00:18:55\",\n        \"max\": \"2022-05-30 23:45:59\",\n        \"num_unique_values\": 2899,\n        \"samples\": [\n          \"2022-03-05 16:55:43\",\n          \"2022-04-30 07:30:03\",\n          \"2022-02-02 01:43:21\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"17_minus\",\n          \"45-54\",\n          \"55-64\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"motivation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"Connect_with_family/friends\",\n          \"Hobby\",\n          \"Friends_Family\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subscription_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"live\",\n          \"core\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geo_area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"ESM\",\n          \"SPM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"learning_activity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"review\",\n          \"live\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"learning_os\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"iOS\",\n          \"Android\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"learning_language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"POR\",\n          \"POL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"session_ended_as\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"missing\",\n          \"aborted\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"session_started_at\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2022-01-01 02:44:39\",\n        \"max\": \"2022-07-23 22:13:24\",\n        \"num_unique_values\": 2900,\n        \"samples\": [\n          \"2022-03-06 16:59:24\",\n          \"2022-05-02 10:29:41\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"session_local_started_at\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-12-31 21:44:39\",\n        \"max\": \"2022-07-23 17:13:24\",\n        \"num_unique_values\": 2881,\n        \"samples\": [\n          \"2022-04-05 13:09:22\",\n          \"2022-05-01 09:03:16.650000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.455207696536254,\n        \"min\": 0.0,\n        \"max\": 58.00992225158153,\n        \"num_unique_values\": 2664,\n        \"samples\": [\n          1.3255204209426648,\n          0.8525527958652274\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# filtered_df[filtered_df['uuid'].isin(unique_uuid)]\n",
        "only_unique_uuid = only_unique_uuid.drop_duplicates(['uuid'])\n",
        "only_unique_uuid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "TeVfCvt33s2S",
        "outputId": "4dc92306-72b7-4e90-c50b-31070aba88a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   uuid\n",
              "subscription_type      \n",
              "core               2858\n",
              "live                 42"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f87a96ce-95c9-40ba-8c27-399028a507df\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>uuid</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subscription_type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>core</th>\n",
              "      <td>2858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>live</th>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f87a96ce-95c9-40ba-8c27-399028a507df')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f87a96ce-95c9-40ba-8c27-399028a507df button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f87a96ce-95c9-40ba-8c27-399028a507df');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-aa787272-13f8-486c-a4bf-0e74f5ca2927\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aa787272-13f8-486c-a4bf-0e74f5ca2927')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aa787272-13f8-486c-a4bf-0e74f5ca2927 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"only_unique_uuid\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"subscription_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"live\",\n          \"core\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1991,\n        \"min\": 42,\n        \"max\": 2858,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          42,\n          2858\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "# ответ на вопрос: сколько клиентов подписаны на тариф core, а сколько на live\n",
        "only_unique_uuid.groupby(['subscription_type']).agg({'uuid':'count'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yYTw3DUqbjgl",
        "outputId": "7488167e-a841-40ab-c4f0-9d72864b4295"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "           uuid\n",
              "age            \n",
              "17_minus   8513\n",
              "18-24     24896\n",
              "25-34      3062\n",
              "35-44     47399\n",
              "45-54     40714\n",
              "55-64     34207\n",
              "65_plus   19342"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7357cb5c-8549-4929-b253-97d31d6be23a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>uuid</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17_minus</th>\n",
              "      <td>8513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18-24</th>\n",
              "      <td>24896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25-34</th>\n",
              "      <td>3062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35-44</th>\n",
              "      <td>47399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45-54</th>\n",
              "      <td>40714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55-64</th>\n",
              "      <td>34207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65_plus</th>\n",
              "      <td>19342</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7357cb5c-8549-4929-b253-97d31d6be23a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7357cb5c-8549-4929-b253-97d31d6be23a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7357cb5c-8549-4929-b253-97d31d6be23a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f2502f89-26d6-454d-85ae-b060a74a085c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f2502f89-26d6-454d-85ae-b060a74a085c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f2502f89-26d6-454d-85ae-b060a74a085c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "number_clients_by_group",
              "summary": "{\n  \"name\": \"number_clients_by_group\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"17_minus\",\n          \"18-24\",\n          \"55-64\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16417,\n        \"min\": 3062,\n        \"max\": 47399,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          8513,\n          24896,\n          34207\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "number_clients_by_group = filtered_df.groupby(['age']).agg({'uuid':'count'})\n",
        "display(number_clients_by_group )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "oM8kCkYhgJsB",
        "outputId": "96ae50fa-fae6-4e2e-d684-317d089783ba"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                       uuid  time_spent\n",
              "age      motivation                                    \n",
              "17_minus Boost_my_career                 97    3.353227\n",
              "         Connect_with_family/friends    233    3.729038\n",
              "         Culture_interest_or_holidays   138    3.579646\n",
              "         Family_Friends                 194    4.709539\n",
              "         Friends_Family                1018    3.404830\n",
              "...                                     ...         ...\n",
              "65_plus  Other                         1018    7.520699\n",
              "         School                          20    4.199806\n",
              "         Skills                        1895    5.138502\n",
              "         Travel                        3471    5.550898\n",
              "         Work                            52    4.251942\n",
              "\n",
              "[85 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ba02568-4765-46a2-b226-9e2fed3e93f7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>uuid</th>\n",
              "      <th>time_spent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <th>motivation</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">17_minus</th>\n",
              "      <th>Boost_my_career</th>\n",
              "      <td>97</td>\n",
              "      <td>3.353227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Connect_with_family/friends</th>\n",
              "      <td>233</td>\n",
              "      <td>3.729038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Culture_interest_or_holidays</th>\n",
              "      <td>138</td>\n",
              "      <td>3.579646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Family_Friends</th>\n",
              "      <td>194</td>\n",
              "      <td>4.709539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Friends_Family</th>\n",
              "      <td>1018</td>\n",
              "      <td>3.404830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">65_plus</th>\n",
              "      <th>Other</th>\n",
              "      <td>1018</td>\n",
              "      <td>7.520699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>School</th>\n",
              "      <td>20</td>\n",
              "      <td>4.199806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skills</th>\n",
              "      <td>1895</td>\n",
              "      <td>5.138502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Travel</th>\n",
              "      <td>3471</td>\n",
              "      <td>5.550898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Work</th>\n",
              "      <td>52</td>\n",
              "      <td>4.251942</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ba02568-4765-46a2-b226-9e2fed3e93f7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7ba02568-4765-46a2-b226-9e2fed3e93f7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7ba02568-4765-46a2-b226-9e2fed3e93f7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bfca9175-52d5-43b7-a38b-e9ad5c326c07\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bfca9175-52d5-43b7-a38b-e9ad5c326c07')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bfca9175-52d5-43b7-a38b-e9ad5c326c07 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "age_groups_clients",
              "summary": "{\n  \"name\": \"age_groups_clients\",\n  \"rows\": 85,\n  \"fields\": [\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2056,\n        \"min\": 11,\n        \"max\": 7541,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          240,\n          97,\n          3455\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6570791982472974,\n        \"min\": 2.7479742685932305,\n        \"max\": 14.027455939173242,\n        \"num_unique_values\": 85,\n        \"samples\": [\n          6.089493388483208,\n          3.3532269958429515,\n          5.229143431973334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# количество клиентов в разрезе групп клиентов, организованных по  мотивации и возрастам\n",
        "age_groups_clients = filtered_df.groupby(['age', 'motivation']).agg({'uuid':'count',\n",
        "                                                'time_spent':'mean'})\n",
        "display(age_groups_clients)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v2cA-b2KZ6Bd"
      },
      "outputs": [],
      "source": [
        "age_groups_clients.to_csv('age_groups_clients.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "X3qNFBjm7gGt",
        "outputId": "6e7a159a-671c-4965-d500-8384daade1aa"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-18-917c95a16b9f>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  info_about_clients['delta_date'] = info_about_clients['session_started_at'] - info_about_clients['subscription_started_at']\n",
            "<ipython-input-18-917c95a16b9f>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  info_about_clients['delta_date'] = info_about_clients['delta_date'].dt.days\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "info_about_clients"
            },
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>uuid</th>\n",
              "      <th>subscription_started_at</th>\n",
              "      <th>age</th>\n",
              "      <th>motivation</th>\n",
              "      <th>subscription_type</th>\n",
              "      <th>geo_area</th>\n",
              "      <th>learning_activity</th>\n",
              "      <th>learning_os</th>\n",
              "      <th>learning_language</th>\n",
              "      <th>session_ended_as</th>\n",
              "      <th>session_started_at</th>\n",
              "      <th>session_local_started_at</th>\n",
              "      <th>time_spent</th>\n",
              "      <th>delta_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>072d948379bbfab373951e1ba4f039e3</td>\n",
              "      <td>2022-01-18 04:29:53</td>\n",
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              "      <td>2022-01-18 04:54:18</td>\n",
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              "      <td>2022-01-02 18:14:28</td>\n",
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              "      <td>2022-03-08 03:07:37</td>\n",
              "      <td>35-44</td>\n",
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              "      <td>core</td>\n",
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              "      <td>completed</td>\n",
              "      <td>2022-03-08 03:40:53</td>\n",
              "      <td>2022-03-07 22:40:53</td>\n",
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              "      <td>lesson</td>\n",
              "      <td>iOS</td>\n",
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              "      <td>completed</td>\n",
              "      <td>2022-03-30 02:49:56</td>\n",
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              "      <td>Interest</td>\n",
              "      <td>core</td>\n",
              "      <td>USA</td>\n",
              "      <td>lesson</td>\n",
              "      <td>iOS</td>\n",
              "      <td>FRA</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-04-22 01:30:12</td>\n",
              "      <td>2022-04-21 21:30:12</td>\n",
              "      <td>2.099788</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104451</th>\n",
              "      <td>e606cad65d6fd3272bf63fa790d79ea1</td>\n",
              "      <td>2022-03-09 09:19:35</td>\n",
              "      <td>18-24</td>\n",
              "      <td>Travel</td>\n",
              "      <td>core</td>\n",
              "      <td>GSM</td>\n",
              "      <td>review</td>\n",
              "      <td>iOS</td>\n",
              "      <td>SPA</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-05-08 16:51:18</td>\n",
              "      <td>2022-05-08 18:51:18</td>\n",
              "      <td>3.342336</td>\n",
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              "      <td>2022-04-17 17:39:45</td>\n",
              "      <td>45-54</td>\n",
              "      <td>Travel</td>\n",
              "      <td>core</td>\n",
              "      <td>GSM</td>\n",
              "      <td>lesson</td>\n",
              "      <td>Android</td>\n",
              "      <td>SPA</td>\n",
              "      <td>matched</td>\n",
              "      <td>2022-06-17 05:10:03</td>\n",
              "      <td>2022-06-17 07:10:03</td>\n",
              "      <td>5.457728</td>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
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              "      <td>2022-03-09 09:19:35</td>\n",
              "      <td>18-24</td>\n",
              "      <td>Travel</td>\n",
              "      <td>core</td>\n",
              "      <td>GSM</td>\n",
              "      <td>lesson</td>\n",
              "      <td>iOS</td>\n",
              "      <td>SPA</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-05-08 16:54:25</td>\n",
              "      <td>2022-05-08 18:54:25</td>\n",
              "      <td>2.178329</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28223</th>\n",
              "      <td>957d5abf4cf7157ca89c6d7d82b2ab52</td>\n",
              "      <td>2022-04-17 17:39:45</td>\n",
              "      <td>45-54</td>\n",
              "      <td>Travel</td>\n",
              "      <td>core</td>\n",
              "      <td>GSM</td>\n",
              "      <td>review</td>\n",
              "      <td>Android</td>\n",
              "      <td>SPA</td>\n",
              "      <td>completed</td>\n",
              "      <td>2022-06-17 05:17:05</td>\n",
              "      <td>2022-06-17 07:17:05</td>\n",
              "      <td>3.669027</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271034</th>\n",
              "      <td>53bf45ce14c7cc4fa41b029cfdb332bb</td>\n",
              "      <td>2022-05-28 01:19:21</td>\n",
              "      <td>35-44</td>\n",
              "      <td>Interest</td>\n",
              "      <td>core</td>\n",
              "      <td>USA</td>\n",
              "      <td>review</td>\n",
              "      <td>iOS</td>\n",
              "      <td>QMS</td>\n",
              "      <td>aborted</td>\n",
              "      <td>2022-07-27 01:32:35</td>\n",
              "      <td>2022-07-26 18:32:35</td>\n",
              "      <td>0.233333</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178133 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e5ba559-a7a9-4894-985a-78a82afec640')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-5e5ba559-a7a9-4894-985a-78a82afec640 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5e5ba559-a7a9-4894-985a-78a82afec640');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e127b71f-2eba-4830-9f11-b7bc7a5568ae')\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e127b71f-2eba-4830-9f11-b7bc7a5568ae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                    uuid subscription_started_at       age  \\\n",
              "1       072d948379bbfab373951e1ba4f039e3     2022-01-18 04:29:53  17_minus   \n",
              "72741   ad8e6a0d0e6b26c8bcac5f6fe219ddbf     2022-01-02 23:03:32     18-24   \n",
              "72739   03e775a37330257b4492d928907a22f7     2022-03-08 03:07:37     35-44   \n",
              "72730   922a28ab119d122ce313d2619cf0876f     2022-03-30 02:30:19     18-24   \n",
              "72725   76819a624214858dbd5a04acb06e08c0     2022-04-22 01:26:06     45-54   \n",
              "...                                  ...                     ...       ...   \n",
              "104451  e606cad65d6fd3272bf63fa790d79ea1     2022-03-09 09:19:35     18-24   \n",
              "256593  957d5abf4cf7157ca89c6d7d82b2ab52     2022-04-17 17:39:45     45-54   \n",
              "138815  e606cad65d6fd3272bf63fa790d79ea1     2022-03-09 09:19:35     18-24   \n",
              "28223   957d5abf4cf7157ca89c6d7d82b2ab52     2022-04-17 17:39:45     45-54   \n",
              "271034  53bf45ce14c7cc4fa41b029cfdb332bb     2022-05-28 01:19:21     35-44   \n",
              "\n",
              "            motivation subscription_type geo_area learning_activity  \\\n",
              "1       Friends_Family              core      USA            lesson   \n",
              "72741         Interest              core      USA            lesson   \n",
              "72739             Work              core      USA            lesson   \n",
              "72730             Work              core      USA            lesson   \n",
              "72725         Interest              core      USA            lesson   \n",
              "...                ...               ...      ...               ...   \n",
              "104451          Travel              core      GSM            review   \n",
              "256593          Travel              core      GSM            lesson   \n",
              "138815          Travel              core      GSM            lesson   \n",
              "28223           Travel              core      GSM            review   \n",
              "271034        Interest              core      USA            review   \n",
              "\n",
              "       learning_os learning_language session_ended_as  session_started_at  \\\n",
              "1              iOS               DEU        completed 2022-01-18 04:54:18   \n",
              "72741          iOS               QMS        completed 2022-01-02 23:14:28   \n",
              "72739          iOS               SPA        completed 2022-03-08 03:40:53   \n",
              "72730          iOS               QMS        completed 2022-03-30 02:49:56   \n",
              "72725          iOS               FRA        completed 2022-04-22 01:30:12   \n",
              "...            ...               ...              ...                 ...   \n",
              "104451         iOS               SPA        completed 2022-05-08 16:51:18   \n",
              "256593     Android               SPA          matched 2022-06-17 05:10:03   \n",
              "138815         iOS               SPA        completed 2022-05-08 16:54:25   \n",
              "28223      Android               SPA        completed 2022-06-17 05:17:05   \n",
              "271034         iOS               QMS          aborted 2022-07-27 01:32:35   \n",
              "\n",
              "       session_local_started_at  time_spent  delta_date  \n",
              "1           2022-01-17 20:54:18    2.431191           0  \n",
              "72741       2022-01-02 18:14:28    2.089713           0  \n",
              "72739       2022-03-07 22:40:53    9.670180           0  \n",
              "72730       2022-03-29 19:49:56    2.470926           0  \n",
              "72725       2022-04-21 21:30:12    2.099788           0  \n",
              "...                         ...         ...         ...  \n",
              "104451      2022-05-08 18:51:18    3.342336          60  \n",
              "256593      2022-06-17 07:10:03    5.457728          60  \n",
              "138815      2022-05-08 18:54:25    2.178329          60  \n",
              "28223       2022-06-17 07:17:05    3.669027          60  \n",
              "271034      2022-07-26 18:32:35    0.233333          60  \n",
              "\n",
              "[178133 rows x 14 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# датафрейм, который описывает поведение всех клиентов обучающей платформы\n",
        "info_about_clients = filtered_df\n",
        "\n",
        "info_about_clients['delta_date'] = info_about_clients['session_started_at'] - info_about_clients['subscription_started_at']\n",
        "info_about_clients['delta_date'] = info_about_clients['delta_date'].dt.days\n",
        "\n",
        "info_about_clients = info_about_clients.sort_values('delta_date')\n",
        "info_about_clients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVxRKtuk6JiW",
        "outputId": "6d651f82-dec2-40d0-d672-8dc87ae43cf6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(18040, 4889, 3353, 2820, 2354)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "active_clients_0_days = info_about_clients[info_about_clients['delta_date'] == 0]\n",
        "active_clients_7_days = info_about_clients[info_about_clients['delta_date'] == 7]\n",
        "active_clients_14_days = info_about_clients[info_about_clients['delta_date'] == 14]\n",
        "active_clients_21_days = info_about_clients[info_about_clients['delta_date'] == 21]\n",
        "active_clients_28_days = info_about_clients[info_about_clients['delta_date'] == 28]\n",
        "\n",
        "count_active_clients_0_days = len(active_clients_0_days)\n",
        "count_active_clients_7_days = len(active_clients_7_days)\n",
        "count_active_clients_14_days = len(active_clients_14_days)\n",
        "count_active_clients_21_days = len(active_clients_21_days)\n",
        "count_active_clients_28_days = len(active_clients_28_days)\n",
        "\n",
        "count_active_clients_0_days, count_active_clients_7_days, count_active_clients_14_days, count_active_clients_21_days, count_active_clients_28_days"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2upbajf22ImC"
      },
      "outputs": [],
      "source": [
        "info_about_clients.to_csv('info_about_clients.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tFoEWu95Xysy"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FCul3YabXyja"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fxb6liOYjxg"
      },
      "source": [
        "исследование датасета, который описывает характеристики клиентов (только уникальные uuid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ObG_GYkQXzNa"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Aj7NLqKpMvsX"
      },
      "outputs": [],
      "source": [
        "# все сессии всех клиентов после фильтрации исходного датасета\n",
        "filtered_by_uuid = pd.read_csv('/content/Filtered UUIDs.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhR9o-0jg2hz",
        "outputId": "1161ee15-3e38-4036-9548-82f87f9002e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'uuid', 'subscription_started_at', 'age', 'motivation',\n",
              "       'subscription_type', 'geo_area', 'learning_activity', 'learning_os',\n",
              "       'learning_language', 'session_ended_as', 'session_started_at',\n",
              "       'session_local_started_at', 'time_spent', 'delta_date'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "filtered_by_uuid.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "j114tdfDXuvM",
        "outputId": "c59ee5cd-3397-49c0-a091-735c0689bc7b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"display(info_about_unique_clients\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"072d948379bbfab373951e1ba4f039e3\",\n          \"bac98c42570186f0b57ff66f38d99314\",\n          \"2ddb86acc0bbfd6b1dd2e9b63963f8b1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"17_minus\",\n          \"45-54\",\n          \"35-44\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"learning_language\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"DEU\",\n          \"QMS\",\n          \"ITA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"motivation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Friends_Family\",\n          \"Work\",\n          \"Travel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"subscription_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"core\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geo_area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"learning_activity\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"review\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"learning_os\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"iOS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-bf68ae3f-58b7-4b96-ad13-39fa1dd5b9d1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>uuid</th>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf68ae3f-58b7-4b96-ad13-39fa1dd5b9d1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                               uuid       age learning_language  \\\n",
              "0  072d948379bbfab373951e1ba4f039e3  17_minus               DEU   \n",
              "1  bac98c42570186f0b57ff66f38d99314     45-54               QMS   \n",
              "2  2ddb86acc0bbfd6b1dd2e9b63963f8b1     35-44               ITA   \n",
              "\n",
              "       motivation subscription_type geo_area learning_activity learning_os  \n",
              "0  Friends_Family              core      USA            lesson         iOS  \n",
              "1            Work              core      USA            review         iOS  \n",
              "2          Travel              core      USA            review         iOS  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# выборка уникальных клиентов\n",
        "dataset_by_unique_uuid = pd.read_csv('/content/Unique clients info.csv')\n",
        "info_about_unique_clients = dataset_by_unique_uuid[['uuid', 'age', 'learning_language','motivation', 'subscription_type', 'geo_area', 'learning_activity', 'learning_os']]\n",
        "\n",
        "display(info_about_unique_clients.iloc[:3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C4MFG6NOPPFu"
      },
      "outputs": [],
      "source": [
        "# @title Default title text\n",
        "# какие языки изучают клиенты. сколько клиентов изучает каждый язык\n",
        "# из каких регионов клиенты\n",
        "# какие языки изучают в каждом регионе\n",
        "# какие активности пользуются наибольшим и наименьшим интересом у клиентов\n",
        "# как распределяются активности в разрезе возрастов\n",
        "# как распределяются активности в разрезе операционных систем\n",
        "# как связаны типы завершения сессий и активности\n",
        "# как связаны типы завершения сессий и активности + операционные системы"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "iIE09jqLPQos",
        "outputId": "449e11e8-4ea4-47b5-ba88-ea1606083e73"
      },
      "outputs": [
        {
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            "text/plain": [
              "             uuid\n",
              "learning_os      \n",
              "iOS          1650\n",
              "Android       795\n",
              "Windows       308\n",
              "Mac OS X      126\n",
              "Linux           7\n",
              "Chrome OS       5"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# какими операционными системами пользуются клиенты\n",
        "pd.DataFrame(info_about_unique_clients.groupby('learning_os').agg({'uuid':'count'})).sort_values('uuid', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "zkob7Ge7PQk1",
        "outputId": "7cf5a6bd-6c80-4dd3-e7e3-cfcc9c402966"
      },
      "outputs": [
        {
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f7aafd34-06f1-4712-ac48-f4295518dc4f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7aafd34-06f1-4712-ac48-f4295518dc4f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3c8a4f94-cb1b-40a2-82a4-3b657fe0e544\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c8a4f94-cb1b-40a2-82a4-3b657fe0e544')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3c8a4f94-cb1b-40a2-82a4-3b657fe0e544 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          uuid\n",
              "geo_area      \n",
              "USA        989\n",
              "GSM        935\n",
              "FIM        444\n",
              "ESM        382\n",
              "SPM        150"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame(info_about_unique_clients.groupby('geo_area').agg({'uuid':'count'})).sort_values('uuid', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "FlNXTOmqhu_W",
        "outputId": "07fa0986-3db7-4470-8f58-01cb0c811b16"
      },
      "outputs": [
        {
          "data": {
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              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"learning_language\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"TUR\",\n          \"DAN\",\n          \"ENG\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 229,\n        \"min\": 6,\n        \"max\": 737,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          34,\n          28,\n          737\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-e3f70997-bc5d-4d9f-b4c3-c53fe521eb97 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-3875b7e1-4777-4ae7-8116-850291948508 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   uuid\n",
              "learning_language      \n",
              "ENG                 737\n",
              "QMS                 550\n",
              "SPA                 420\n",
              "FRA                 342\n",
              "ITA                 337\n",
              "DEU                 161\n",
              "RUS                  93\n",
              "POR                  72\n",
              "NLD                  39\n",
              "TUR                  34\n",
              "SWE                  30\n",
              "DAN                  28\n",
              "POL                  26\n",
              "NOR                  24\n",
              "IND                   6"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# какие языки изучают клиенты\n",
        "pd.DataFrame(info_about_unique_clients.groupby('learning_language').agg({'uuid':'count'})).sort_values('uuid', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "MnVYWOTiPQhp",
        "outputId": "cd7c07af-3161-4cb6-8c4f-4ddd2e5d3012"
      },
      "outputs": [
        {
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              "</div>\n",
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            ],
            "text/plain": [
              "                            uuid\n",
              "geo_area learning_language      \n",
              "USA      QMS                 515\n",
              "GSM      ENG                 367\n",
              "FIM      ENG                 234\n",
              "GSM      SPA                 154\n",
              "         ITA                 137\n",
              "...                          ...\n",
              "FIM      SWE                   1\n",
              "SPM      TUR                   1\n",
              "         SWE                   1\n",
              "GSM      IND                   1\n",
              "USA      TUR                   1\n",
              "\n",
              "[68 rows x 1 columns]"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "geo_area_learn_language = pd.DataFrame(info_about_unique_clients.groupby(['geo_area', 'learning_language']).agg({'uuid':'count'})).sort_values('uuid', ascending=False)\n",
        "geo_area_learn_language"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_MstbAORPQd_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "66CZeltBPQan"
      },
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      "source": []
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    {
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          "height": 455
        },
        "id": "57pyaXutikzO",
        "outputId": "2a820d9f-e23e-434e-9b57-816e54af4964"
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      "outputs": [
        {
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              "summary": "{\n  \"name\": \"grouped_filtered_info_about_clients\",\n  \"rows\": 380,\n  \"fields\": [\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 126,\n        \"min\": 1,\n        \"max\": 882,\n        \"num_unique_values\": 162,\n        \"samples\": [\n          92,\n          142,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2d1247b7-6c37-46bc-8e42-f74d211186fe button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2d1247b7-6c37-46bc-8e42-f74d211186fe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fdbb5cba-4986-4861-a0b0-8740c1260a16\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fdbb5cba-4986-4861-a0b0-8740c1260a16')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fdbb5cba-4986-4861-a0b0-8740c1260a16 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                 uuid\n",
              "age      motivation                  delta_date      \n",
              "17_minus Boost_my_career             0             31\n",
              "                                     7              1\n",
              "                                     28             2\n",
              "         Connect_with_family/friends 0             17\n",
              "                                     14             2\n",
              "...                                               ...\n",
              "65_plus  Travel                      14           114\n",
              "                                     21            57\n",
              "                                     28            38\n",
              "         Work                        0             18\n",
              "                                     7              1\n",
              "\n",
              "[380 rows x 1 columns]"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# сводная таблица, описывающая кол-во клиентов в разрезе дней с даты подписки на сервис и мотивации клиента\n",
        "filtered_info_about_clients = info_about_clients[info_about_clients['delta_date'].isin([0, 7, 14, 21, 28])]\n",
        "grouped_filtered_info_about_clients = filtered_info_about_clients.groupby(['age', 'motivation', 'delta_date']).agg({'uuid':'count'})\n",
        "grouped_filtered_info_about_clients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1EUBvEHFkWMc"
      },
      "outputs": [],
      "source": [
        "grouped_filtered_info_about_clients.to_csv('grouped_filtered_info_about_clients.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ay6F8eX28OdX"
      },
      "source": [
        "### Исследование датафрейма"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sxV6vM2I7HW9"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CjMXqlci8bjY"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RVUSo-v58VnM"
      },
      "outputs": [],
      "source": [
        "all_sessions = pd.read_csv('')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Znb_TvDtbZjo",
        "outputId": "9afccb1e-535e-455d-b1c4-f7760c14b483"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'uuid', 'age', 'motivation', 'geo_area', 'learning_os',\n",
              "       'subscription_type', 'learning_language', 'learning_activity',\n",
              "       'subscription_month', 'subscription_day', 'subscription_time',\n",
              "       'session_local_started_at', 'session_month', 'session_day',\n",
              "       'session_time', 'time_spent', 'session_ended_as'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "all_sessions.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O1O1fML2bJfj"
      },
      "outputs": [],
      "source": [
        "all_sessions = all_sessions[['uuid', 'age', 'motivation', 'geo_area', 'learning_os',\n",
        "       'subscription_type', 'learning_language', 'learning_activity','session_local_started_at','time_spent', 'session_ended_as']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dItngJMaAYFb"
      },
      "outputs": [],
      "source": [
        "all_sessions['subscription_started_at'] = pd.to_datetime(all_sessions['subscription_started_at'])\n",
        "all_sessions['session_started_at '] = pd.to_datetime(all_sessions['session_started_at'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CmZTCEKEBR5p"
      },
      "outputs": [],
      "source": [
        "all_sessions['delta_day'] = all_sessions['session_started_at '] - all_sessions['subscription_started_at']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7D59JhYlBbjV"
      },
      "outputs": [],
      "source": [
        "all_sessions.drop(columns=['Unnamed: 0', 'delta_date'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0OFJMyVATAc",
        "outputId": "3d5cdb04-cbc6-47ff-cb80-5a0204254bb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 178133 entries, 0 to 178132\n",
            "Data columns (total 15 columns):\n",
            " #   Column                    Non-Null Count   Dtype          \n",
            "---  ------                    --------------   -----          \n",
            " 0   uuid                      178133 non-null  object         \n",
            " 1   subscription_started_at   178133 non-null  datetime64[ns] \n",
            " 2   age                       178133 non-null  object         \n",
            " 3   motivation                178133 non-null  object         \n",
            " 4   subscription_type         178133 non-null  object         \n",
            " 5   geo_area                  178133 non-null  object         \n",
            " 6   learning_activity         178133 non-null  object         \n",
            " 7   learning_os               177662 non-null  object         \n",
            " 8   learning_language         177973 non-null  object         \n",
            " 9   session_ended_as          178133 non-null  object         \n",
            " 10  session_started_at        178133 non-null  object         \n",
            " 11  session_local_started_at  177010 non-null  object         \n",
            " 12  time_spent                178133 non-null  float64        \n",
            " 13  session_started_at        178133 non-null  datetime64[ns] \n",
            " 14  delta_day                 178133 non-null  timedelta64[ns]\n",
            "dtypes: datetime64[ns](2), float64(1), object(11), timedelta64[ns](1)\n",
            "memory usage: 20.4+ MB\n"
          ]
        }
      ],
      "source": [
        "all_sessions.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9P5aAv33NDvh",
        "outputId": "733521d8-5aaf-4dd3-8100-413e686de7d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "количество клиентов: 2900\n"
          ]
        }
      ],
      "source": [
        "number_unique_clients = len(all_sessions['uuid'].unique())\n",
        "print(f'количество клиентов: {number_unique_clients}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "CvpQpPo1N3_D",
        "outputId": "87223e3d-bec6-49eb-ff7c-c4ae92e5c3ea"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>uuid</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17_minus</th>\n",
              "      <td>8513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18-24</th>\n",
              "      <td>24896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25-34</th>\n",
              "      <td>3062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35-44</th>\n",
              "      <td>47399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45-54</th>\n",
              "      <td>40714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55-64</th>\n",
              "      <td>34207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65_plus</th>\n",
              "      <td>19342</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "age\n",
              "17_minus     8513\n",
              "18-24       24896\n",
              "25-34        3062\n",
              "35-44       47399\n",
              "45-54       40714\n",
              "55-64       34207\n",
              "65_plus     19342\n",
              "Name: uuid, dtype: int64"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "number_clients = all_sessions.groupby(['age'])['uuid'].agg('count')\n",
        "number_clients"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppRYRWoSOXKJ",
        "outputId": "eff564b4-148e-42b1-da62-09961683ca1e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "178133"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "number_clients.sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q20OKsWu-Gj1"
      },
      "outputs": [],
      "source": [
        "all_sessions['delta_day'] = all_sessions['delta_day'].dt.days"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-adgHBMjIsoT",
        "outputId": "a355d11d-01dd-4f2e-db10-02372ff904d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "кол-во сессий, которые завершены с ошибкой: 69238\n",
            "кол-во сессий, которые успешно завершены: 108895\n"
          ]
        }
      ],
      "source": [
        "all_sessions.sort_values('delta_day', ascending=False)\n",
        "\n",
        "ended_missing = all_sessions[all_sessions['session_ended_as'] != 'completed']\n",
        "ended_completed = all_sessions[all_sessions['session_ended_as'] == 'completed']\n",
        "count_missing_session = len(ended_missing)\n",
        "count_completed_session = len(ended_completed)\n",
        "print(f'кол-во сессий, которые завершены с ошибкой: {count_missing_session}\\nкол-во сессий, которые успешно завершены: {count_completed_session}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mpiVffEXGGoR"
      },
      "outputs": [],
      "source": [
        "# уникальные uuid\n",
        "unique_uuid = info_about_clients['uuid'].unique()\n",
        "count_unique_clients_by_delta_date = info_about_clients[info_about_clients['uuid'].isin(unique_uuid)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r24YpEsjLqOA"
      },
      "outputs": [],
      "source": [
        "max_sessions = count_clients_by_delta_date.sort_values(ascending=False).values[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "xrvZ3Y0-G0YH",
        "outputId": "f57bd27c-decc-47db-e335-6955a8d454ae"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "count_unique_clients_by_delta_date.groupby(['delta_day'])['uuid'].agg('count').plot(kind='line', color='green', alpha=.7, marker='.', title='Количество активных клиентов в разрезе delta_time', fontsize='9')\n",
        "\n",
        "plt.xticks(np.arange(0, 70, 7))\n",
        "plt.yticks(np.arange(0, max_sessions, 1000))\n",
        "plt.xlabel('count clients')\n",
        "plt.ylabel('delta day')\n",
        "plt.grid(alpha=.7)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-yLF_zAtzpx3"
      },
      "outputs": [],
      "source": [
        "grouped_df = ordered_df.groupby(['uuid','age', 'motivation', 'subscription_type', 'geo_area', 'learning_os']).agg({'time_spent':'sum'})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q12s9zlVyEWK"
      },
      "source": [
        "## upgrade dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tiLqIe9rnFXS"
      },
      "outputs": [],
      "source": [
        "# upgrd_df = pd.read_csv('')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L5056H9eCiDw"
      },
      "outputs": [],
      "source": [
        "# upgrd_df = датафрейм с включенными в него месяцем, датой, временем начала подписки\n",
        "# upgrd_df = ordered_df\n",
        "\n",
        "upgrd_df['subscription_started_at'] = pd.to_datetime(upgrd_df['subscription_started_at'])\n",
        "upgrd_df['session_started_at'] = pd.to_datetime(upgrd_df['session_started_at'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "qKittYVSDs4W",
        "outputId": "49152974-0a81-46ce-ee3e-586e87f99698"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "upgrd_df"
            },
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>uuid</th>\n",
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              "      <th>session_ended_as</th>\n",
              "      <th>...</th>\n",
              "      <th>time_spent</th>\n",
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              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>subscription_month</th>\n",
              "      <th>subscription_day</th>\n",
              "      <th>subscription_time</th>\n",
              "      <th>session_month</th>\n",
              "      <th>session_day</th>\n",
              "      <th>session_time</th>\n",
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              "      <td>eee65d5b85267cd5b3938c90d85893a0</td>\n",
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              "      <td>eee65d5b85267cd5b3938c90d85893a0</td>\n",
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              "      <td>core</td>\n",
              "      <td>USA</td>\n",
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              "      <td>Windows</td>\n",
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              "    </tr>\n",
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              "<p>3 rows × 22 columns</p>\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2b7d6cea-0abc-4081-8526-65dfaf2bf7bc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2b7d6cea-0abc-4081-8526-65dfaf2bf7bc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0145d0c7-c04d-4a2e-b7e5-bd6d9276c883\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0145d0c7-c04d-4a2e-b7e5-bd6d9276c883')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0145d0c7-c04d-4a2e-b7e5-bd6d9276c883 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                    uuid subscription_started_at      age  \\\n",
              "2806    eee65d5b85267cd5b3938c90d85893a0     2022-01-01 00:18:55  65_plus   \n",
              "3320    eee65d5b85267cd5b3938c90d85893a0     2022-01-01 00:18:55  65_plus   \n",
              "194038  eee65d5b85267cd5b3938c90d85893a0     2022-01-01 00:18:55  65_plus   \n",
              "\n",
              "       motivation subscription_type geo_area learning_activity learning_os  \\\n",
              "2806     Interest              core      USA            review         iOS   \n",
              "3320     Interest              core      USA            review     Windows   \n",
              "194038   Interest              core      USA            review     Windows   \n",
              "\n",
              "       learning_language session_ended_as  ... time_spent month  day  \\\n",
              "2806                 FRA          missing  ...   1.966250     1    1   \n",
              "3320                 FRA        completed  ...   0.225406     1    1   \n",
              "194038               FRA        completed  ...   3.669493     1    1   \n",
              "\n",
              "            time  subscription_month subscription_day  subscription_time  \\\n",
              "2806    00:18:55                   1                1           00:18:55   \n",
              "3320    00:18:55                   1                1           00:18:55   \n",
              "194038  00:18:55                   1                1           00:18:55   \n",
              "\n",
              "        session_month session_day     session_time  \n",
              "2806                2          13         01:08:12  \n",
              "3320                1          30  21:24:30.410000  \n",
              "194038              1          18  01:05:06.806000  \n",
              "\n",
              "[3 rows x 22 columns]"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# выделение параметров: дата, месяц, время начала подписки\n",
        "upgrd_df['subscription_month'] = upgrd_df['subscription_started_at'].dt.month\n",
        "upgrd_df['subscription_day'] = upgrd_df['subscription_started_at'].dt.day\n",
        "upgrd_df['subscription_time'] = upgrd_df['subscription_started_at'].dt.time\n",
        "\n",
        "\n",
        "# выделение параметров: дата, месяц, время начала подписки\n",
        "upgrd_df['session_month'] = upgrd_df['session_started_at'].dt.month\n",
        "upgrd_df['session_day'] = upgrd_df['session_started_at'].dt.day\n",
        "upgrd_df['session_time'] = upgrd_df['session_started_at'].dt.time\n",
        "\n",
        "\n",
        "upgrd_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "imG1TD71B6Q8",
        "outputId": "c559a9e6-eedc-4654-ad9c-f24f3cd2fc22"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"count_sessions_df\",\n  \"rows\": 2900,\n  \"fields\": [\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2900,\n        \"samples\": [\n          \"12823ad72cf9f574076ab9dea001ed2e\",\n          \"77cc4726ddee8f4811ab0e0cd624e884\",\n          \"9d98c67f395a29e68a76e9f7242e9af5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count_sessions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 118,\n        \"min\": 1,\n        \"max\": 3335,\n        \"num_unique_values\": 336,\n        \"samples\": [\n          314,\n          247,\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "count_sessions_df"
            },
            "text/html": [
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              "      <th>count_sessions</th>\n",
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              "      <th>uuid</th>\n",
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              "      <td>994</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-511f97ca-d772-42fb-969c-39b437919fe3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-511f97ca-d772-42fb-969c-39b437919fe3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-511f97ca-d772-42fb-969c-39b437919fe3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d915dd07-d5c2-41c9-a43a-fa46d0f7b8e1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  count_sessions\n",
              "uuid                                            \n",
              "2115f2292d60c559aaf0e8c6c07e8ab0            3335\n",
              "0f720834fd28863eb5df9d964496e50f            1811\n",
              "f8114081ae74a6e529219b033ee0ba34             994"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# вычисление кол-ва сессий на клиента\n",
        "count_sessions_df = pd.DataFrame(upgrd_df.groupby(['uuid']).agg({'uuid':'count'})).rename(columns={'uuid':'count_sessions'}).sort_values('count_sessions', ascending=False)\n",
        "count_sessions_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-hR60uGMRAib"
      },
      "outputs": [],
      "source": [
        "# выгрузка датасета upgrd_df\n",
        "# upgrd_df.to_csv('upgrd_df.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "UAKQKXP47PQT",
        "outputId": "ed202bb1-e207-469d-aed5-55ad63b2967c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"grouped_ordered_df\",\n  \"rows\": 2900,\n  \"fields\": [\n    {\n      \"column\": \"uuid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2900,\n        \"samples\": [\n          \"0b3277c964ebf1327cbfa0b37cff5da8\",\n          \"858501e75efedc9d7220c81fdc51579d\",\n          \"29306ec3ea6591c678f94cf1737aaae4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count_sessions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 118,\n        \"min\": 1,\n        \"max\": 3335,\n        \"num_unique_values\": 336,\n        \"samples\": [\n          245,\n          556,\n          638\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 534.7749565141039,\n        \"min\": 0.0,\n        \"max\": 12247.699877351391,\n        \"num_unique_values\": 2893,\n        \"samples\": [\n          3.6913193860264393,\n          14.100438720011564,\n          11.446051306627528\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "grouped_ordered_df"
            },
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              "      <th>count_sessions</th>\n",
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              "<p>2900 rows × 2 columns</p>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d9fd39e-312d-4c50-af12-8081b47c8037');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-34bcb616-7ab7-47f5-9837-8315a6710066 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                  count_sessions   time_spent\n",
              "uuid                                                         \n",
              "001d357d114acce8661069c8b40b2bff              34   140.852433\n",
              "0021f3d96885b4d76f93ff4e044c39f7              60   219.775454\n",
              "006a07306a35da73484f0db0d98174cb              63   236.281579\n",
              "00737f5186cfd6853ea01b654e3c278d              13    28.769826\n",
              "00831285665c81cf0dc2b829e3bb6fc5               7    42.373935\n",
              "...                                          ...          ...\n",
              "ff78b2311421b913f4df6195a09f9ef5             190  1050.602083\n",
              "ffa12c1ebef9e6090f740a3158c47261             117   367.780068\n",
              "ffb6e6abf7be67177810ec16489971e8             178   734.232116\n",
              "ffc82a8ddb1e4e0753d11285780bec75              12    36.869839\n",
              "ffef88d71275c33a6e1a4915f1db19a4              16    25.135514\n",
              "\n",
              "[2900 rows x 2 columns]"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grouped_ordered_df = pd.DataFrame(upgrd_df.groupby(['uuid']).agg({'uuid':'count','time_spent':'sum'}))\n",
        "\n",
        "grouped_ordered_df = grouped_ordered_df.rename(columns={'uuid':'count_sessions'})\n",
        "grouped_ordered_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K7D73nE6UMSr"
      },
      "outputs": [],
      "source": [
        "# выгрузка датасета, в котором описано кол-во сессий на клиента, суммарным инвестированным в обучение временем на пользователя\n",
        "grouped_ordered_df.to_csv('count_sessions_by_uuid.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OPPecQcHO1Ep"
      },
      "outputs": [],
      "source": [
        "grouped_ordered_df['month'] = grouped_ordered_df['subscription_started_at'].dt.month\n",
        "grouped_ordered_df['day'] = grouped_ordered_df['subscription_started_at'].dt.day\n",
        "grouped_ordered_df['time'] = grouped_ordered_df['subscription_started_at'].dt.time\n",
        "# grouped_ordered_df.sort_values('count_sessions', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LCxHjbTbhpgv"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmHXdZ80qTV0"
      },
      "source": [
        "# Ответы на вопросы"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSkjA1F4p-IG"
      },
      "source": [
        "1. Учитывая имеющиеся у вас данные, как бы вы описали поведение пользователя, которое указывает на успешную активацию?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SgySkm4-p3S3"
      },
      "outputs": [],
      "source": [
        "# частота учебных сессий"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Eq0WOVHbqJwq"
      },
      "outputs": [],
      "source": [
        "# время сессии по каждой сессии каждого пользователя"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cag8n_W43Djf"
      },
      "outputs": [],
      "source": [
        "# типы завершения сессий: количество по каждому типу"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h2O-44uL3O7T"
      },
      "outputs": [],
      "source": [
        "# сколько раз пользователь использовал разные типы активностей"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKnwzqHdqJD1"
      },
      "source": [
        "Чем отличается процесс раннего вовлечения в разные сегменты обучающихся?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aO2NaGqjqKSR"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hwmi45ysqKJK"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFoIt2K3qKyE"
      },
      "source": [
        "Как используются интерактивные занятия в сочетании с другими видами обучения? Ведут ли подписчики в режиме реального времени что-то иное, чем другие подписчики?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iXdaT1shqNOo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m4KPiPm5qNhY"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Sw1Vm6vqQLu"
      },
      "source": [
        "Предположим, что команда планирует разработать экспериментальную программу обучения, направленную на активизацию новых учащихся в течение первых трех недель после регистрации. Команда хочет Проводить A / B тестирование и повторять использование функции каждые 2 недели, чтобы они не могли оптимизировать ее на основе трехнедельного запаздывания. Основываясь на вашем опыте и предоставленном наборе данных, что что бы вы посоветовали команде сделать? Есть ли какие-либо риски, о которых им следует знать?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SCFMqcpBqPjV"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jOhAxNlY809E"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6r3GyF3y801w",
        "outputId": "2a3aab5e-edee-46cd-9536-1aef8d6a0823"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(414.29, 4.86)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "number_clients_list = [827, 612, 565, 425, 230, 199, 42]\n",
        "average_time_spent_list = [7.4, 5.5, 4.7, 4.3, 4.2, 4.1, 3.8]\n",
        "\n",
        "mean_num_clients = np.array(number_clients_list).mean().round(2)\n",
        "mean_average_time_spent_list = np.array(average_time_spent_list).mean().round(2)\n",
        "\n",
        "mean_num_clients, mean_average_time_spent_list"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "AiJvOW_Mq5hQ",
        "q12s9zlVyEWK"
      ],
      "provenance": [],
      "mount_file_id": "1oC1p6NxT7bFJvcfoq6v0f_uAhRSwFFUT",
      "authorship_tag": "ABX9TyM1XNwvFKf13lA4Cv3ONuDq",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}